The present invention relates in general to an image processing technique and, more particularly, to a technique of aligning a plurality of picture images for image synthesis.
Recently, it has become important to synthesize a plurality of picture images, for example, two images picked up at different times. Some image processes apply to a medical imaging system such as a computed tomography (CT) scanner, which obtains an image of a selected plane section of a human body. One of these image processes obtains the difference between two images of a region of interest (e.g., an affected part) in the selected plane section in order to more clearly show a change in such a region over a period of time. Such an image process, considered as one of picture image synthesis, requires the precise alignment of two images to produce a subtraction image. Poor image alignment cannot result in a good subtraction image.
The simplest image alignment is based on the visual judgement of a human being. This alignment involves the comparison of two images, detection of misalignment (or mis-registration) between them, and transition of one of the images according to the misalignment. The image transition includes a simple coordinate conversion such as parallel transition, rotation, enlargement and reduction. This alignment technique is relatively effective when the misalignment, which is expressed in a vector quantity, is uniform over the entire image plane. A single picture image obtained for medical purposes (or a single satellite-transmitted picture image) is usually a complex of different sub-regions of an object so that the vector of the misalignment between two such picture images is hardly uniform over the entire image plane. That is, regional variations in misalignment vector often appear. In this case, the simple coordinate conversion technique cannot be effective in aligning two images over the entire image plane.
There is a known method for solving the problem. This method includes the steps of:
(1) Dividing both picture images of an object into several sub-image regions.
(2) Computing a correlation coefficient for each pair of corresponding sub-image regions of two picture images.
(3) Detecting the misalignment vector between that pair of sub-image regions which has the maximum correlation coefficient.
(4) Obtaining the misalignment over the entire image based on the detected vector. According to this method, however, when the misalignment between corresponding sub-image regions is significantly small or a contrast difference between two images is small, the variation in the correlation coefficients is considerably small. This reduces the detection sensitivity of the misalignment vector and deteriorates alignment accuracy as a consequence. In this case, if the images have noise components, the significance of the variation in the correlation coefficients may be counteracted. Such an adverse effect of the noise greatly decreases the alignment accuracy and may result in alignment error at the worst.